1. Field of the Invention
The present invention relates to an early-warning apparatus for health detection of a servo motor and a method for operating the same, and more particularly to an early-warning apparatus for health detection of the servo motor and the method for operating the same for analyzing a vibration signal which is transformed in time and frequency domains.
2. Description of Prior Art
With the advancement of the high-efficient and high-quality production technologies, the machinery equipment will overall be developed toward the trend of large scale, high speed, systematization, complication, and automation. Thus, the correlation between every individual system is highly dependent with the large and complicated machinery equipment. However, if the hidden faults of the machinery equipment do not be detected before actually occurring in the future, the economical losses will be considerable.
The application of the CNC tool machine is exemplified for further demonstration. An upper controller is provided to send position commands to a multi-shaft servo driver to drive servo motors. A work table is moved through a transmission system (including screw rods, slide rails, and so on) of the CNC tool machine. However, the problems such as machine and lubrication consumption would influence smoothness of the work table because of using the CNC tool machine for a long time. Thus, irregular vibration and energy consumption of the machine table are unavoidable. When the abnormal vibration exists in the machinery equipment, the machinery equipment would be normally operated during a short time but the machinery equipment will be inevitably damaged for a long time operation.
For estimating the health of the servo motors by a systematic operation, the PC-based schemes are usually adopted. The calculators collect the data of voltage, current, and losses, and the vibration data sent from accelerometers of the machine table. These time-domain data can be transformed into frequency-domain or time-and-frequency-domain data by using a fast Fourier transform (FFT) or a wavelet transform (WT). Although the health index can be calculated by statistical methods and model learning, multiple stand-alone calculators have to be used because of large amount of computation. Thus, the total equipment costs and the required space will increase due to the additional amount of calculators. In addition, the different functions of the drivers provided by different brands (even different types) would limit the uniformity and reality of the captured signals.
The researches of failure diagnosis of rotating machinery have been developed for many years. So far the fast Fourier transform (FFT), which is provided to process signals and analyze data, is most commonly used in estimating the vibration signals, and more particularly in the frequency-domain analysis. Traditional Fourier spectral analysis conveniently provides energy distribution of processed signals in the frequency domain by linearly superimposing the processed signals, which are composed of sine and cosine functions with different frequencies, magnitudes, and phases. Thus, the signal features can be inherently represented in the frequency domain by the Fourier spectral analysis for processing linear and stationary signals, while that are difficult to analyze in the time domain.
However, for analyzing the non-linear and non-stationary signals, the Fourier spectral analysis has the following disadvantages:
1. During the integration process, however, some messages of the processed signals would be easily erased. Besides, the spectrum illusions will result in incorrect spectrum, thus making mistakes of estimating the processed signals.
2. The time-domain information of the signals will disappear when the time-domain signals are transformed into the frequency-domain signals. That is, it is not available to confirm the occurrence time of the specific frequency spectrum in the frequency domain.
The wavelet transform (WT) can be also provided to analyze signals in three-dimensional components (including time, frequency, and magnitude components). A composite signal with different frequencies can be decomposed into a number of signals with corresponding independent frequencies, thus effectively separating the signal and the noise among the composite signal. Because the wavelet transform, however, is derived from the Fourier spectral analysis, it inherently has the energy-distributing, bandwidth-rising, and adaptability-lacing disadvantages. In addition, a number of basis functions have to be selected before analyzing all of the data of the processed signals, thus it will limit applicable scopes.
Accordingly, it is desirable to provide an early-warning apparatus for health detection of a servo motor and a method for operating the same to dispense with additional external sensors; and further estimate the vibration phenomenon of the servo motor of the CNC tool machine and analyze non-linear and non-stationary characteristics of the estimated vibration phenomenon.